This is a sample AI Spotlight report showing the kind of findings a real session surfaces. Every number, pattern, and recommendation below came directly from the client's own data — not assumptions. Client name and business name have been redacted for privacy.
Before the session, the client exported three data files from their existing tools. No new software. No IT setup. Everything analysed came from tools they already use every day.
The AI ran pattern analysis across all three data sources and surfaced these findings in order of financial impact.
The quote log shows 187 quotes sent in 14 months. Cross-referencing with the owner's time log, each quote takes an average of 3.2 hours to prepare — site visit notes, material pricing, labour estimate, formatting, sending. At a conservative value of $65/hr for owner time, that's $38,400 per year spent on quotes alone.
The QuickBooks data shows 11 invoices totalling $22,100 that remained unpaid for over 60 days in the last 12 months. Of those, 4 were eventually written off. The pattern: jobs completed in summer had 2.3x more late payments than spring or fall — likely due to customer cash flow timing. There was zero automated follow-up in the email data.
An automated follow-up sequence — day 7, day 14, day 30, day 60 — with a direct payment link would have recovered an estimated $18,400 of the $22,100 based on industry recovery rates for polite automated reminders.
The time log shows the owner spends an average of 14 hours per week on scheduling — assigning crews to jobs, coordinating vehicle availability, handling customer calls about arrival times, and rescheduling when weather delays hit. This is the single largest block of non-billable time in the business.
Based on job history patterns in the data, an AI scheduling layer could auto-assign 80%+ of jobs using crew skills, location, job type, and vehicle availability — reducing owner involvement to exception handling only.
Cross-referencing invoice amounts against material costs and labour hours logged, the AI identified that flat roof repairs, emergency call-outs, and gutter replacements are priced on average 18–24% below the break-even margin after accounting for actual labour time. Shingle replacement and full re-roofs are correctly priced.
The customer data shows 68 jobs completed between 18 and 36 months ago where the customer has not received any follow-up contact. Based on typical roofing maintenance cycles, these customers are statistically in the window for a gutter clean, inspection, or minor repair. At an average job value of $480, a re-engagement campaign targeting these 68 customers represents a potential $32,640 in recoverable revenue.
These aren't projections — they're calculations from the client's actual data over the past 14 months.
These are the three moves that deliver the fastest return based on the data. They don't require new software — they plug into what the client already uses.
Every finding in this report came from three exported files and one session. No assumptions. No industry averages. Just your numbers, analysed by AI, explained in plain English.